Enhancing-ARDS diagnostics for ICU patients: a retrospective, nested case-control study to develop a biomarker-based model

Abstract Background: To investigate whether a series of biomarkers including club cell protein 16 (CC16), angiopoietin 2(Ang-2), soluble receptor for advanced glycation end-products (sRAGE), high-mobility group box 1 protein (HMGB1), and surfactant protein D (SPD) could be utilized for identifying patients, thereby increasing the diagnostic value of acute respiratory distress syndrome(ARDS) in intensive care unit (ICU). Methods: 211 ICU admissions were enrolled in this retrospective, nested case-control study. These patients were then divided into ARDS (n=79) and non-ARDS (n=132) groups according to the Berlin criteria on ICU day 1. Patient characteristics, vital signs, and laboratory examinations were collected within three hours of admission. Five inflammatory associated plasma biomarkers, as well as lung epithelial and endothelial injury which included CC16, Ang-2, sRAGE, HMGB1 and SPD were measured in the morning of day two in the ICU. Diagnostic values were analyzed with receiver operating characteristic (ROC) curves. Pearson’s product-moment correlation coefficient and multivariate logistic regression analysis were applied for predictive purposes. Results: C-reactive protein (CRP), NT-proBNP, and PH values for traditional indicators and five biomarkers were analyzed with an objective ARDS indicator, the PaO2/FiO2 ratio. Evidence suggests that only four of potential indicators analyzed here, and CRP hold high diagnostic value. The area under curve (AUC) for each were as follows: CC16 (AUC: 0.752; 95%CI0.680-0.824), Ang-2 (AUC: 0.695; 95%CI 0.620 -0.770), HMGB1 (AUC: 0.668; 95%CI 0.592-0.744), sRAGE (AUC: 0.665; 95%CI 0.588-0.743), CRP (AUC: 0.701; 95%CI 0.627-0.776). No single indicator surpassed the diagnostic capability of the PaO2/FiO2 ratio which had an AUC: 0.844(95%CI 0.789-0.898), especially in terms of sensitivity. However, when the binary logistic model was transformed and the model was built, the AUC increased from 0.647(95%CI 0.568-0.726) to 0.911(95%CI 0.864-0.946). Among the combinations tested, PaO2/FiO2+CRP+Ang-2+CC16+HMGB1 resulted in an AUC of 0.910 (95%CI 0.863-0.945), while PaO2/FiO2+CRP+Ang-2+CC16+HMGB1+sRAGE+SPD have an AUC of 0.911(95%CI 0.864-0.946). Conclusions: A combination of the assessed biomarkers could enhance ARDS diagnostics, which has obvious ramifications for patient care and prognosis. It may be possible to develop a predictive ARDS nomogram; however, of the combinations tested here, we would recommend PaO2/FiO2+CRP+Ang-2+CC16+HMGB1 for clinical practice. This is because of the cost implications in contrast with the benefit involved in utilizing the more elaborate model. Although, further health economics research is required to consider this opportunity cost for emergency care policy.